Power grids have not undergone significant architectural changes since use of electricity for power was realized more than a century ago. The idea of a “Smart Grid” was introduced in the late 1990s, however, today, power grids still only employ limited intelligence in managing and providing power to consumers. Energy transmission and distribution systems are currently at a crossroads, as they confront the significant problem of imbalances of various kinds Not only is the gap between supply and demand continuing to increase due to global population growth, but there also is a geographic imbalance in energy production and consumption patterns. These imbalances and uncertainties could be exacerbated in the future considering the rapidly increasing energy demands of newly industrialized nations, such as China, India, Brazil, and Russia, as these and other nations will compete for more generating sources to meet expected energy demands. While incorporating a wide variety of renewable (non-fossil fuel) energy sources is part of the solution to the increasing energy demands, it is not likely that incorporating renewable energy sources will be a panacea for the impending energy issues. Thus, it is clear that there also will have to be significant changes in the power transmission and distribution network to help meet future energy needs.
Conventional power grids typically employ Supervisory Control and Data Acquisition/Energy Management System (SCADA/EMS) technology, which collects information regarding conditions in the power grid from remote terminal units or remote control centers at scan rates ranging on the order of multiple seconds. The power systems are typically dealing with a couple of hundred of thousands data points updated in correspondence with the multiple second scan rates.
From a network security and reliability point of view, state estimator algorithms have been improved over the years but are still using well known techniques. The creation of Regional Transmission Operator in the United States pushed the technology to improve efficiency of the algorithms to cope with 30,000+ network buses, to address topology errors and parameter estimation. Contingency analysis application is also very similar to what it used to be 20 years ago.
From a dispatcher point of view, the most frequently used displays within a control room are still the schematic representation of the network (e.g., mapboard), the substation online displays and tabular alarm lists. While such arrangement used to be sufficient, with the increased energy demands, increased risk of outages due to the increase in energy demands, increased complexity in transmission network systems, new power flow patterns starting to emerge following the introduction of deregulated markets, introduction of more and more intermittent resources (e.g., distributed power generation, such as wind power, solar power, etc.) to the power grid, etc., such conventional arrangement is becoming increasingly lacking in key features, which eventually may lead to dramatic consequences. For example, after Aug. 15, 2003, blackout in Northern America, a post-mortem analysis clearly highlighted the fact that SCADA/EMS systems had severe deficiencies. Overwhelming alarm flows and lack of Situation Awareness have been emphasized.
For grid reliability, transmission system operators desire to have an accurate up-to-date representation of their power system. Conventionally, the SCADA system collects data from the field and EMS applications perform security analysis of the current power system state. The power system state is compared against “normal” operating conditions, e.g., one needs to make sure that each piece of equipment is operated according to its nominal design conditions while ensuring that enough reserve and security margins are still available even after a loss of a major component on the network. This process is usually called “Network Monitoring”. Today, the Network Monitoring function does not include a monitoring of the health of the pieces of equipment that constitute the topology of the network under operation.
Further, SCADA/EMS systems operation also has not evolved significantly over the last few decades, since being implemented. It actually mimics vertically integrated utilities which even after deregulation (split of generation, transmission and distribution) often operate their system in a centrally hierarchical system. Typical configurations consist of a National Control Center supervising Regional Control Centers, each of them interacting with some local Load Dispatch Centers with functions performed independently with few if not no bidirectional data flow and functional responsibilities.
Further, today, SCADA/EMS systems are mostly focusing on operation, using data collected on the order of multiple second scan rates, and provide limited insight in developing or short-medium term changing conditions. This used to be an acceptable arrangement, until recently, since network operation planning was well established and did not change drastically between day-ahead scheduling and real-time operation. Now, with the ever increasing uncertainties impacting grid operation such as intermittent resources production, obtaining only a current view of the power system is no longer adequate for efficient and reliable network operation.
As indicated above, there have been certain drivers for changes in power distribution and transmission. For instance, new power flow patterns have started to emerge following the introduction of deregulated markets. Those have been more and more accentuated with the interdependency of markets (regional markets) and development of close to real-time markets (infra-day markets). In addition, uncertainty has also been introduced with the extensive development of renewable energy resources production such as wind power. Such intermittent resources require careful attention especially for reserve management and network security. Currently, situation awareness of the fast changing flow patterns and other aspects of the power grid are lacking.
This tendency is also emphasized with the development of Flexible AC Transmission Systems (FACTS) and High Voltage DC links (HVDC) equipment. Thanks to the use of modern power electronics technologies, operators currently benefit from flexible ways to re-dispatch flows. However, this flexibility propagates to neighboring networks and can thereby contribute to uncertainty and fast changes of flow patterns if not coordinated properly. This is also re-enforced by the ever larger interconnection of transmission networks which ease the propagation of grid disturbances (e.g. inter area oscillation) and make them visible and detrimental to other grid operators.
Another driver for change is the fast development of Distributed Generation (DG). In some European countries where most of the wind generation is connected at distribution level, it is quite frequent to see energy flowing from distribution back to sub-transmission and transmission levels. Associated with the limited predictability of the wind resources, such unknown represents a risk factor for network security since this production may not be necessarily observable. Moreover, conventional power generation management is based on the management of centralized power plants rather than decentralized power plants, and decentralized management is becoming increasingly necessary. For example, in a country such as Denmark, this means shifting from a model to be managed from a couple of tens generators to 5000+ generators. With the technical evolution DG can be eligible to support frequency regulation effort as well as voltage regulation effort. Characteristics of changes brought by massive DG deployment can be summarized as follows: centralized vs. distributed injection points; exponential increase of generation resources; and intermittency of the resources for some of them.
The above specificities make these evolutions hardly manageable by conventional SCADA/EMS systems. DG impacts each SCADA/EMS subsystem. It is necessary for a data acquisition system to connect these resources which are owned by many different actors, and hence represent as many external systems to connect, while ensuring security and reliability of the data acquisition. Network security also requires accurate modeling of the distributed generation injections to be able to assess their impact on steady-state network state as well as for dynamic analysis. Generation control and management applications also need to cope with these DG resources especially dealing with the uncertainty related to the intermittent character of some DG resources.
In some countries, high growth areas can stress the system and push operation towards previously unattained limits. In many countries, while the electric energy consumption growth is stable, peak demand growth rate now represents a challenge. As a consequence, with the ever increasing time to build new transmission lines infrastructure due in part to local opposition to such construction (e.g., “Not In My Back Yard” mindset), system operators must operate the grid with existing and in some cases old assets. This often translates in using the assets at their maximum capacity and making them sweat. This puts the grid operation at hedge and requires deep knowledge of the real state of the assets. Current power system applications are not capable of optimizing the use of the current grid infrastructure.
The above obviously shall not jeopardize the grid reliability. With the ever growing economy sensitivity to the electricity availability, grid operation at the edge shall not occult grid reliability principles and deteriorate electricity quality of supply. With increasing threats of major blackouts and pressing incentives to improve revenue performance, transmission system operators face daunting challenges.
In recent years, manufacturers have developed condition monitoring solutions which aim at locally measuring key vital parameters of network equipments via smart sensors. Real-time calculation and simulation of developing conditions can then be performed to support assessment of the real-state of the assets. Condition monitoring systems apply to a large spectrum of network assets, such as switching devices or transformers. They can provide data usually called “non-operational” data to supervisory systems. Those data are not necessarily electrical values but are also encompassing information related to the real state and health of the power grid asset and are relevant to its optimized management.
With the latest development of international standards, one can also notice that the substations do not behave anymore as an independent and local actor in charge of reporting information to a hierarchical upper level. More and more intelligence is introduced at the substation level. For instance, IEC61850, in its latest evolutions, enables communications between substations which thus allows intelligent and fast adapting scheme to better protect and operate the grid. The emergence of so-called “local” agents leads to a much more decentralized information and operation architecture and represents a real paradigm shift from the centrally hierarchical grid operation which applied for decades.
The development of Active Distribution Networks and eventual involvement and empowerment of end-user for consumption behavior will also drive significant changes in grid operation and will as a consequence impact the SCADA/EMS systems. However, conventional generation management systems do not have adequate demand response programs and lack the ability to forecast the impact of demand response on generation dispatch and on reserves management, and its impact on network analysis, and lack adequate ability to control the associated resources and provision of information necessary for demand response settlement.
There have been recent technological advancements, which can or are being utilized in power grids. For instance, advancements in communication technology and infrastructure now allow for relatively large bandwidth for data transmission as well as high quality of service to support critical applications. Also, sensors, such as Non-Conventional Instrument Transformer, also have been recently developed to ease monitoring of existing assets with non-intrusive techniques. Further, enhanced processing power brought by continuous improvements in hardware and availability of data management capabilities allow now to gather more data and make them available to an end-user for application usage.
Another recent advancement is Phasor Measurement Units (PMU), which is a relatively new data source at the control center level due in part to the improvement of telecommunication infrastructures. By providing coherent set of time-correlated measurements, PMU brings a new insight of “real” network state which until very recently was inaccessible at the grid operator level. Also, Intelligent Equipment Devices (IED) and Digital Fault Recorders are other examples of data sources of interest to understand a network state. However, conventional power generation management systems do not adequately leverage the respective capabilities of these recent technological advancements.
The above-described deficiencies of today's systems are merely intended to provide an overview of some of the problems of conventional systems, and are not intended to be exhaustive. Other problems with the state of the art and corresponding benefits of some of the various non-limiting embodiments may become further apparent upon review of the following detailed description.